Exosome miR-23a-3p from Osteoblast Alleviates Spinal Cord Ischemia/Reperfusion Injury by Down-Regulating KLF3-Activated CCNL2 Transcription

Abstract

Background: Spinal cord ischemia/reperfusion injury (SCIRI) is usually caused by spinal surgery or aortic aneurysm surgery and can eventually lead to paralysis or paraplegia and neurological dysfunction. Exosomes are considered as one of the most promising therapeutic strategies for SCIRI as they can pass the blood-spinal barrier. Previous studies have proved that exosomes secreted by osteocytes have a certain slowing effect on SCIRI. Aim: We aimed to explore the effect of osteoblast secreted exosomes on SCIRI. Methods: Firstly, neurons and osteoblasts were co-cultured under different conditions. GEO database was utilized to detect the expression of miR-23a-3p in osteoblast exosomes. SCIRI cells were treated with exosomes, and the detection was taken to prove whether miR-23a-3p could slow the progression of SCIRI. Downstream gene and the potential regulatory mechanism were explored through database and functional experiments. Results: MiR-23a-3p was highly expressed in exosomes and it slowed down the process of SCIRI. Downstream mRNA KLF3 could bind to miR-23a-3p and was highly expressed in IRI. Moreover, CCNL2 was regulated by KLF3 and was highly expressed in IRI. Rescue experiments verified that miR-23a-3p suppressed the transcription of CCNL2 by targeting KLF3. Conclusion: Exosome miR-23a-3p from osteoblast alleviates SCIRI by down-regulating KLF3-activated CCNL2 transcription.

Efficacy of miRNA-modified mesenchymal stem cell extracellular vesicles in spinal cord injury: A systematic review of the literature and network meta-analysis

“…EVs could be used to treat spinal cord contusion, compression, and transection (Mu et al, 2021 ). Furthermore, they could treat spinal cord ischemia/reperfusion injury because of their ability to pass the blood-spinal barrier (Wu et al, 2022 ). A meta-analysis showed that EVs were more effective in treating contusion models as compared with compression models (Yi and Wang, 2021 ).…”